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[SSS11-P14] Basic study of a real-time damage estimation system for liquefaction
Keywords:liquefaction, damage estimation, real-time
The real-time earthquake damage estimation system (Nakamura et al., 2013) makes it possible to immediately estimate damage to buildings, etc. due to earthquake motion in units of towns. It is being used for decision-making during disaster response. However, regarding the liquefaction that occurs due to earthquakes, it is not possible to provide damage estimation information of buildings for now, although there are development examples of liquefaction prediction systems such as Kamiya et al. (2013). Since the damage caused by liquefaction has many effects on various structures and lifelines, it is an urgent task to establish a damage estimation method for early recovery.
Therefore, we targeted three earthquakes, the Niigata-ken Chuetsu Earthquake, the Tohoku-Pacific Ocean Earthquake, and the Kumamoto Earthquake, and we developed the liquefaction estimation system mainly based on the liquefaction estimation method of Senna et al. (2018). After acquiring strong motion observation records on K-NET and KiK-net, an index deltaIs obtained from a 10-second integrated value of the real-time seismic intensity (Kunugi et al., 2008) exceeding a threshold (4.5) is calculated and interpolated by the IDW method. After that, a liquefaction occurrence probability in 250m mesh units is calculated by multiplying the regression coefficient α, which differs depending on a region and a microtopography classification group (Matsuoka et al., 2011). In addition, we developed the risk estimation system calculating the total destruction rate of the building due to liquefaction based on the method of Shimizu et al. (2016), by multiplying the liquefaction occurrence probability, the liquefaction area ratio (Yamamoto et al., 2009) and the total collapse rate of the building when liquefaction occurs (Cabinet Office, 2003). These estimation results are stored as a data file in netCDF format, and it is possible to visualize the spatial distribution on a Web browser or a GIS software (Fig. 1).
Next, as a result of comparing the distribution of the liquefaction occurrence rate of the 250 m mesh calculated by the system with the 250 m liquefaction mesh based on aerial photograph interpretation (Wakamatsu et al., 2017), it was confirmed that concerning each of the tree earthquakes, the mesh estimated to have a liquefaction occurrence rate of 2% or more contains about 80 to 90% of the liquefaction mesh within the blue frame area of Fig.1, and this method is effective in grasping the distribution of liquefaction damage. In addition, the 250m mesh was aggregated for each 10km mesh, and the average value of the estimation results by the system was compared with the ratio of the liquefied mesh to the 10km mesh. As a result, a certain correlation was confirmed between the two. On the other hand, in the Kumamoto earthquake, problems such as underestimation of the estimated value were confirmed, and it was found that more appropriate parameter setting is necessary in consideration of geomorphological factors.
In the future, in addition to developing a more precise prediction model of liquefaction occurrence rate based on recent earthquake damages, we are going to construct a real-time liquefaction damage estimation system that can be used for quick understanding of damage status and early restoration of social infrastructure. In addition, regarding building damage caused by liquefaction, sufficient observation data could not be obtained to verify the estimated value, but verification of the possibility of using it as data that contributes to risk evaluation will be an issue.
Therefore, we targeted three earthquakes, the Niigata-ken Chuetsu Earthquake, the Tohoku-Pacific Ocean Earthquake, and the Kumamoto Earthquake, and we developed the liquefaction estimation system mainly based on the liquefaction estimation method of Senna et al. (2018). After acquiring strong motion observation records on K-NET and KiK-net, an index deltaIs obtained from a 10-second integrated value of the real-time seismic intensity (Kunugi et al., 2008) exceeding a threshold (4.5) is calculated and interpolated by the IDW method. After that, a liquefaction occurrence probability in 250m mesh units is calculated by multiplying the regression coefficient α, which differs depending on a region and a microtopography classification group (Matsuoka et al., 2011). In addition, we developed the risk estimation system calculating the total destruction rate of the building due to liquefaction based on the method of Shimizu et al. (2016), by multiplying the liquefaction occurrence probability, the liquefaction area ratio (Yamamoto et al., 2009) and the total collapse rate of the building when liquefaction occurs (Cabinet Office, 2003). These estimation results are stored as a data file in netCDF format, and it is possible to visualize the spatial distribution on a Web browser or a GIS software (Fig. 1).
Next, as a result of comparing the distribution of the liquefaction occurrence rate of the 250 m mesh calculated by the system with the 250 m liquefaction mesh based on aerial photograph interpretation (Wakamatsu et al., 2017), it was confirmed that concerning each of the tree earthquakes, the mesh estimated to have a liquefaction occurrence rate of 2% or more contains about 80 to 90% of the liquefaction mesh within the blue frame area of Fig.1, and this method is effective in grasping the distribution of liquefaction damage. In addition, the 250m mesh was aggregated for each 10km mesh, and the average value of the estimation results by the system was compared with the ratio of the liquefied mesh to the 10km mesh. As a result, a certain correlation was confirmed between the two. On the other hand, in the Kumamoto earthquake, problems such as underestimation of the estimated value were confirmed, and it was found that more appropriate parameter setting is necessary in consideration of geomorphological factors.
In the future, in addition to developing a more precise prediction model of liquefaction occurrence rate based on recent earthquake damages, we are going to construct a real-time liquefaction damage estimation system that can be used for quick understanding of damage status and early restoration of social infrastructure. In addition, regarding building damage caused by liquefaction, sufficient observation data could not be obtained to verify the estimated value, but verification of the possibility of using it as data that contributes to risk evaluation will be an issue.